An adaptive data gathering strategy for target tracking in cluster-based wireless sensor networks

In a typical cluster-based sensor network, the data is usually gathered and fused on cluster heads. In target tracking applications, a target is often detected by sensor nodes in multiple clusters, leading to redundant data transmissions through multiple paths from the cluster heads to the data sink. To reduce such redundant data transmissions and thus to save energy, this paper proposes an adaptive data gathering strategy, called ADGS. Our novel idea is to adaptively select one node with the most residual energy and the least communication cost from the active nodes around the target. This node is responsible for gathering and aggregating the data from the other active nodes and is therefore called Aggregation Node (AN). The aggregated data is then transmitted only from the AN to the sink. Our experiments demonstrate that the proposed approach achieves a significant reduction in power consumption for data transmission and prolongs the network lifetime by 857.6% and 85.8% compared to two state-of-the-art data gathering approaches.